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From |
Maarten Buis <maartenlbuis@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Simplification of formula in logistic regression |

Date |
Mon, 16 May 2011 09:35:06 +0200 |

--- On Sun, May 15, 2011 at 4:23 PM, Mikkel Brabrand wrote: > If I want clinicians to use my model, it needs to be simple. I cannot expect them to use a piece of software to calculate the risk score and it is virtually impossible to have it incorporated in the programs used at my department. I therefore need to simplify it and make the variables categorized or dichotomous. Splitting up your variable is not the only way to make your results understandable to a lay public. Adding a continuous variable and than choosing and tabulating a couple of well chosen example values or a carefully designed graphs can do just as well or even better. See for example: Nicola Orsini and Sander Greenland (2011) "A procedure to tabulate and plot results after flexible modeling of a quantitative covariate" The Stata Journal, 11(1): 1--29. http://www.stata-journal.com/article.html?article=st0215 > I have previously used the trial and error way, and come up with a model that seems reasonable (and tested it in an independent cohort, and am now testing it in two external cohorts at other hospitals). However, there must be a correct way to select the cuf-off levels, I just cannot find out how. I have asked most statisticians I have met on my way, but no one seems to know how. I hoped that some of you might have a suggestion... This can be thought of as a zero-th order spline with unknown knot location. Within linear regression there is a tradition of using -nl- to find such knot locations. The problem is that in order to generalize that to logistic regression you would want to use -ml- and the the first and second derivatives of the likelihood function with respect to the knot location are not continuous functions. This makes it impossible to use the standard machinery of finding the maximum-likelihood solution, and even harder to do inference on it. So I would just forget about that. Hope this helps, Maarten -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Simplification of formula in logistic regression***From:*Mikkel Brabrand <mikkel@brabrand.net>

**Re: st: Simplification of formula in logistic regression***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: Simplification of formula in logistic regression***From:*Mikkel Brabrand <mikkel@brabrand.net>

**Re: st: Simplification of formula in logistic regression***From:*Nick Cox <njcoxstata@gmail.com>

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